GraphRNN2/main_baselines/graphvae/test.py

import numpy as np
import torch


def tril_indices(rows, cols, offset=0):
    return torch.ones(rows, cols, dtype=torch.uint8).tril(offset).nonzero()


# x = torch.tensor([1., 2., 3., 4., 5., 6.])
# m = torch.zeros((3, 3))
# rows=3
# cols=3
# offset=0
# tril_indices = torch.ones(rows, cols, dtype=torch.uint8).tril(offset).nonzero()
# m[tril_indices[0], tril_indices[1]] = x
# print(m)
def sym(A):
    for i in range(A.shape[0]):
        for j in range(A.shape[1]):
            A[j, i] = A[i, j]
    return A


# dm = np.random.rand(6)
# tri = np.zeros((3, 3))
# print(tri)
# print(np.triu_indices(3))
# print(dm)
# tri[np.triu_indices(3)] = dm
# print(tri)
# A = sym(tri)
# print(A)
a = np.zeros((3,3))
a[0,1] = 5
a[2,2] = 6

print(a)
print(np.where(~a.any(axis=1))[0])
missing_node_index = np.where(~a.any(axis=1))[0][0]
print(a.shape)
print(a[missing_node_index, :])